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Goal-Driven Autonomy with Case-Based Reasoning

  • Héctor Muñoz-Avila
  • Ulit Jaidee
  • David W. Aha
  • Elizabeth Carter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6176)

Abstract

The vast majority of research on AI planning has focused on automated plan recognition, in which a planning agent is provided with a set of inputs that include an initial goal (or set of goals). In this context, the goal is presumed to be static; it never changes, and the agent is not provided with the ability to reason about whether it should change this goal. For some tasks in complex environments, this constraint is problematic; the agent will not be able to respond to opportunities or plan execution failures that would benefit from focusing on a different goal. Goal driven autonomy (GDA) is a reasoning framework that was recently introduced to address this limitation; GDA systems perform anytime reasoning about what goal(s) should be satisfied [4]. Although promising, there are natural roles that case-based reasoning (CBR) can serve in this framework, but no such demonstration exists. In this paper, we describe the GDA framework and describe an algorithm that uses CBR to support it. We also describe an empirical study with a multiagent gaming environment in which this CBR algorithm outperformed a rule-based variant of GDA as well as a non-GDA agent that is limited to dynamic replanning.

Keywords

Plan Execution Plan Repair Opponent Team Hierarchical Task Network Hierarchical Task Network Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Héctor Muñoz-Avila
    • 1
  • Ulit Jaidee
    • 1
  • David W. Aha
    • 2
  • Elizabeth Carter
    • 1
  1. 1.Department of Computer Science & EngineeringLehigh UniversityBethlehem
  2. 2.Naval Research Laboratory (Code 5514)Navy Center for Applied Research in Artificial IntelligenceWashington

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